In the last decade the requirements faced by traditional relational database applications have changed significantly. Most importantly, databases must operate with predictable performance and low administration cost. Furthermore, databases must be able to handle diverse, evolving workloads as applications are constantly extended with new functionality and new data services are deployed, thereby adding new types of queries to the workload in an unpredictable way. Most notably, these new requirements have been expressed in the context of well known commercial platforms with a worldwide presence such as eBay, Amazon, Salesforce, etc. The latter, for instance, allows users to customize their application and define their own queries. Providing such a platform involves highly diverse query workloads; yet, users of the platform expect a constant response time. Unfortunately, throughput and latency guarantees are difficult to make with traditional database systems. These systems are designed to achieve best performance for every individual query. To this end, they rely on sophisticated query optimizers and skilled administrators for selecting the right indexes and materialized views. Such complex systems are expensive to maintain and do not exhibit predictable performance for unpredictable, evolving workloads.
It is thus an object of the invention to overcome the above limitations in disclosing a scalable, distributed relational table and storage full-scan engine capable of sustaining large numbers of diverse queries and updates with guaranteed access latency and data freshness irrespective of the types of workload and queries they have to deal with.
Further objects, features and advantages of the present invention will become apparent to the ones skilled in the art upon examination of the following description in reference to the accompanying drawings. It is intended that any additional advantages be incorporated herein.